mapclassify.HeadTailBreaks

class mapclassify.HeadTailBreaks(y)[source]

Head/tail Breaks Map Classification for Heavy-tailed Distributions

Parameters
yarray

(n,1), values to classify

Notes

Head/tail Breaks is a relatively new classification method developed for data with a heavy-tailed distribution.

Implementation based on contributions by Alessandra Sozzi <alessandra.sozzi@gmail.com>.

For theoretical details see [Jia13].

Examples

>>> import numpy as np
>>> import mapclassify as mc
>>> np.random.seed(10)
>>> cal = mc.load_example()
>>> htb = mc.HeadTailBreaks(cal)
>>> htb.k
3
>>> htb.counts
array([50,  7,  1])
>>> htb.bins
array([ 125.92810345,  811.26      , 4111.45      ])
>>> np.random.seed(123456)
>>> x = np.random.lognormal(3, 1, 1000)
>>> htb = mc.HeadTailBreaks(x)
>>> htb.bins
array([ 32.26204423,  72.50205622, 128.07150107, 190.2899093 ,
       264.82847377, 457.88157946, 576.76046949])
>>> htb.counts
array([695, 209,  62,  22,  10,   1,   1])
Attributes
ybarray

(n,1), bin ids for observations,

binsarray

(k,1), the upper bounds of each class

kint

the number of classes

countsarray

(k,1), the number of observations falling in each class

__init__(self, y)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, y)

Initialize self.

find_bin(self, x)

Sort input or inputs according to the current bin estimate

get_adcm(self)

Absolute deviation around class median (ADCM).

get_fmt(self)

get_gadf(self)

Goodness of absolute deviation of fit

get_legend_classes(self[, fmt])

Format the strings for the classes on the legend

get_tss(self)

Total sum of squares around class means

make(\*args, \*\*kwargs)

Configure and create a classifier that will consume data and produce classifications, given the configuration options specified by this function.

plot(self, gdf[, border_color, …])

Plot Mapclassiifer NOTE: Requires matplotlib, and implicitly requires geopandas dataframe as input.

set_fmt(self, fmt)

table(self)

update(self[, y, inplace])

Add data or change classification parameters.

Attributes

fmt